SPECTRE: A Game Theoretic Framework for Preventing Collusion in Security Games (Demonstration)

ABSTRACT Several models have been proposed for Stackelberg security games (SSGs) and protection against perfectly rational and bounded rational adversaries; however, none of these existing models addressed the destructive cooperation mechanism between adversaries. SPECTRE (Strategic Patrol planner to Extinguish Collusive ThREats) takes into account the synergistic destructive collusion among two groups of adversaries in security games. This framework is designed for the purpose of efficient patrol scheduling for security agents in security games in presence of collusion and is mainly build up on game theoretic approaches, optimization techniques, machine learning methods and theories for human decision making under risk. The major advantage of SPECTRE is involving real world data from human subject experiments with participants on Amazon Mechanical Turk (AMT)

Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data

Wildlife conservation organizations task rangers to deter and capture wildlife poachers. Since rangers are responsible for patrolling vast areas, adversary behavior modeling can help more effectively direct future patrols. In this innovative application track paper, we present an adversary behavior modeling system, INTERCEPT (INTERpretable Classification Ensemble to Protect Threatened species), and provide the most extensive evaluation in the AI literature of one of the largest poaching datasets from Queen Elizabeth National Park (QENP) in Uganda, comparing INTERCEPT with its competitors; we also present results from a month-long test of INTERCEPT in the field. We present three major contributions. First, we present a paradigm shift in modeling and forecasting wildlife poacher behavior. Some of the latest work in the AI literature (and in Conservation) has relied on models similar to the Quantal Response model from Behavioral Game Theory for poacher behavior prediction. In contrast, INTERCEPT presents a behavior model based on an ensemble of decision trees (i) that more effectively predicts poacher attacks and (ii) that is more effectively interpretable and verifiable. We augment this model to account for spatial correlations and construct an ensemble of the best models, significantly improving performance. Second, we conduct an extensive evaluation on the QENP dataset, comparing 41 models in prediction performance over two years. Third, we present the results of deploying INTERCEPT for a one-month field test in QENP - a first for adversary behavior modeling applications in this domain. This field test has led to finding a poached elephant and more than a dozen snares (including a roll of elephant snares) before they were deployed, potentially saving the lives of multiple animals - including elephants.Engineering and Applied Science

Evaluation of the Effect of Combination Therapy on Treatment of COVID-19: A Cohort Study

Background: COVID-19 is a new disease for which a definitive treatment has not yet been proposed. Objectives: The present study aimed to investigate the effect of combination therapy on the treatment of COVID-19 due to the importance of finding an appropriate treatment for this epidemic disease. Methods: This two-center cohort study included 175 confirmed COVID-19 inpatients at two medical centers designated for the treatment of COVID-19 patients in Qom and Qazvin, Iran. In this study, four different groups of drug regimens were studied which included G1 (azithromycin, prednisolone, and naproxen), G2 (lopinavir/ritonavir, azithromycin, naproxen, and prednisolone), G3 (hydroxychloroquine, azithromycin, naproxen, and prednisolone), and G4 (levofloxacin, vancomycin, hydroxychloroquine, and oseltamivir). It should be noted that G1, G2, G3, and G4 treatment regimens were used on 48, 39,30, and 77 patients, respectively. Results: The study participants included 175 confirmed COVID-19 patients with mean±SD age of 58.9 ±15.1 years, out of whom 80 (46%) patients were male and the rest were females. The results indicated that the hospital stay period was significantly shorter in the G1 compared to other groups (G1:5.9±2.4, G2:8.1±4.2, G3: 6.3±1.7, and G4: 6.4±2.9; [P-value=0.008]). It should be noted that pulse rate, oxygen saturation, hemoglobin, and platelet count (PLT) changed significantly during the study in four treatment groups; however, a significant change in temperature, creatinine, and white blood cell (WBC) was observed only in G3, G4, and G1 groups, respectively. The number of ICU admissions and deaths were not statistically significant among the patients who received the four treatment regimens (P=0.785). Based on the results, the history of ischemic heart disease, baseline oxygen saturation, WBC, neutrophil, lymphocyte count, and C-reactive protein (CRP) are the risk factors for the prolonged hospital stay in COVID-19 patients. Conclusion: The obtained results in this study indicated that the combination of azithromycin, prednisolone, and naproxen is the most effective regimen for the treatment of COVID-19, compared to three other combination treatment regimens. Keywords: Anti-inflammatory drugs, Antiviral drugs, Combination therapy, Corticosteroid, COVID-19, Immunomodulators drug

Spatio-Temporal Model for Wildlife Poaching Prediction Evaluated Through a Controlled Field Test in Uganda

Worldwide, conservation agencies employ rangers to protect conservation areas from poachers. However, agencies lack the manpower to have rangers effectively patrol these vast areas frequently. While past work has modeled poachers’ behavior so as to aid rangers in planning future patrols, those models’ predictions were not validated by extensive field tests. In my thesis, I present a spatio-temporal model that predicts poaching threat levels and results from a five-month field test in Uganda’s Queen Elizabeth Protected Area (QEPA). To my knowledge, this is the first time that a predictive model has been evaluated through such an extensive field test in this domain. These field test will be extended to another park in Uganda, Murchison Fall Protected Area, shortly. Main goals of my thesis are to develop the best performing model in terms of speed and accuracy and use such model to generate efficient and feasible patrol routes for the park rangers

Study disaster preparedness of libraries in Qazvin University of Medical Sciences, Iran (2017)

Background: Iran is an emergency state, and natural disaster such as; flood, earthquake or volcano may be occur any time. Therefore, libraries should be prepared for these incidents. Objective: The purpose of this study was to assess the disaster preparedness of libraries in Qazvin University of Medical Sciences, Iran. Methods: This applied research with analytical survey method was conducted in the libraries of Qazvin University of Medical Sciences, Iran in 2017. The study tool was a researcher-made questionnaire that included 5 sections and 57 questions in three dimensions; building, management and staff in evaluating the preparedness of the libraries against unexpected incidents. Validity of the questionnaire was confirmed by librarian and statistics and its reliability was 0.85 based on Cronbach's alpha coefficient. Findings: Preparedness of the libraries of Qazvin University of Medical Sciences against flood, earthquake, fire, storm and thunder was 25%, 20%, 14%, 8% and 5%. The percentage of the libraries preparedness against all disasters were 14.4%, indicating a poor librarian’s readiness. Conclusion: The results showed that university’s readiness was weak in unexpected events and should provide a plan for natural disasters effectively. Keywords: Disasters, Libraries, Earthquakes, Floods, Fire

Where there's Smoke, there's Fire: Wildfire Risk Predictive Modeling via Historical Climate Data

Wildfire is a growing global crisis with devastating consequences. Uncontrolled wildfires take away human lives, destroy millions of animals and trees, degrade the air quality, impact the biodiversity of the planet and cause substantial economic costs. It is incredibly challenging to predict the spatio-temporal likelihood of wildfires based on historical data, due to their stochastic nature. Crucially though, the accurate and reliable prediction of wildfires can help the stakeholders and decision-makers take timely, strategic and effective actions to prevent, detect and suppress the wildfires before they become unmanageable. Unfortunately, most previous studies developed predictive models that suffer from some shortcomings: (i) they do not take the temporal aspects into account precisely and they assume the independent and identically distributed random variables in the evaluation phase; (ii) they do not evaluate their approaches comprehensively, thus it is not clear if their proposed predictions and selected models are reliable across different locations and time steps for practical deployment; and (iii) for the supervised learning models, they use predictor features and fire observations from the same time step in the training phase, which makes the inference task infeasible for future fire prediction. In this paper, we revisit the wildfire predictive modeling, explore the inherent challenges from a practical perspective and evaluate our modeling approach comprehensively via historical burned areas, climate and geospatial data from three vast landscapes in India